When a service is established in an optical network, a light path between the source of a signal and a destination node/nodes of a service needs to be computed. A light path may traverse a sequence of network objects connected by optical waveguides between the source and the destination node through which optical signals can travel. As the optical signal traverses the optical network objects, its quality may degrade due to the physical impairments imposed. Various optical performance parameters for measuring the optical signal quality exist, such as the optical signal to noise ratio (OSNR). If the OSNR is maintained above a given threshold, the correct detection of the optical signal is feasible. Otherwise, the light path is assumed to be unfeasible. An unfeasible light path may be made feasible by means of restoration of the distorted signals such as by means of 3R regeneration (comprising a re-shaping, a re-amplification and a re-timing of the distorted signal). However, 3R regeneration requires an intermediate node with optical-to-electrical-to-optical conversion capabilities and hence enhances the complexity and cost of the network. Network operators therefore try to reduce the number of transponders and 3R regenerators in the network, which requires a careful and accurate estimation of the optical performance and feasibility of light paths already at the planning stage and also during network operation. Transponders may be understood to realize “electrical-to-optical conversion” at the start node and “optical-to-electrical conversion” at the end node, whereas 3R regenerators perform “optical-to-electrical-to-optical conversion” at an intermediate node.
During network operation, the service setup time should be small and the control plane needs to be capable of quickly considering multiple paths using different 3R placement solutions, such as to restore the network service in case of sudden interruptions of individual network links or to establish a new service.
Optical performance estimation generally involves the assessment of the quality of the data channel and depends on a variety of aspects, such as the physical parameters of the network objects, the length of the optical multiplexing sections (OMS), the type of fibers that are used, the bit rate, the modulation format, wavelength, the type and number of channels, etc. The estimation of the optical performance of a given light path hence involves complex and time consuming computations. This poses challenges both for offline network planning and for online network restoration and/or operation.
Two approaches are conventionally used to estimate the optical performance and to check the feasibility of a light path: (i) a posteriori assessment via running a simulation or another optical performance model, and (ii) a priori assessment by computing all possible light paths in advance. However, both techniques have significant drawbacks. A posteriori calculation during routing is very time-consuming and generally makes use of various approximations to speed up the computation, which can introduce inaccuracies, thereby potentially making both single-layer and multi-layer planning inaccurate. A priori calculation is less time-consuming during routing, at the expense of additional offline computation effort in advance. However, the amount of information that needs to be (firstly) imported and (secondly) processed and utilized can be significant. Usually, large databases are needed in the form of a list or a reachability graph, and assembling and searching in the list/graph per routing instance is required, which slows down the routing process.
In summary, an assessment of the optical performance with high accuracy involves significant computational effort and huge amounts of stored data. Conversely, reducing the computational effort and the amount of data generally implies a loss of accuracy.
What is needed is an improved technique for optical performance estimation that is both accurate and requires less computational and storage resources.